Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory78.3 KiB
Average record size in memory80.1 B

Variable types

Numeric6
Categorical2
Boolean2

Alerts

diastolic_bp is highly overall correlated with systolic_bpHigh correlation
hdl is highly overall correlated with ldlHigh correlation
ldl is highly overall correlated with hdl and 1 other fieldsHigh correlation
systolic_bp is highly overall correlated with diastolic_bpHigh correlation
total_cholesterol is highly overall correlated with ldlHigh correlation
diabetes is highly imbalanced (56.4%) Imbalance
total_cholesterol has unique values Unique
ldl has unique values Unique
hdl has unique values Unique
systolic_bp has unique values Unique
diastolic_bp has unique values Unique

Reproduction

Analysis started2025-01-31 14:12:59.981497
Analysis finished2025-01-31 14:13:08.396850
Duration8.42 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct72
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.886
Minimum18
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-01-31T14:13:08.593577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile26
Q140
median49
Q359
95-th percentile74
Maximum94
Range76
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.209466
Coefficient of variation (CV)0.28483874
Kurtosis-0.21313862
Mean49.886
Median Absolute Deviation (MAD)10
Skewness0.15807184
Sum49886
Variance201.90891
MonotonicityNot monotonic
2025-01-31T14:13:09.008049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53 36
 
3.6%
47 33
 
3.3%
49 33
 
3.3%
41 32
 
3.2%
57 31
 
3.1%
46 30
 
3.0%
59 27
 
2.7%
40 27
 
2.7%
38 26
 
2.6%
54 26
 
2.6%
Other values (62) 699
69.9%
ValueCountFrequency (%)
18 8
0.8%
19 3
 
0.3%
20 3
 
0.3%
21 5
0.5%
22 8
0.8%
23 7
0.7%
24 7
0.7%
25 3
 
0.3%
26 8
0.8%
27 6
0.6%
ValueCountFrequency (%)
94 1
 
0.1%
92 1
 
0.1%
88 2
0.2%
87 3
0.3%
85 2
0.2%
84 3
0.3%
83 2
0.2%
82 4
0.4%
81 4
0.4%
80 4
0.4%

sex
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Male
527 
Female
473 

Length

Max length6
Median length4
Mean length4.946
Min length4

Characters and Unicode

Total characters4946
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 527
52.7%
Female 473
47.3%

Length

2025-01-31T14:13:09.292857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-31T14:13:09.392821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 527
52.7%
female 473
47.3%

Most occurring characters

ValueCountFrequency (%)
e 1473
29.8%
a 1000
20.2%
l 1000
20.2%
M 527
 
10.7%
F 473
 
9.6%
m 473
 
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4946
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1473
29.8%
a 1000
20.2%
l 1000
20.2%
M 527
 
10.7%
F 473
 
9.6%
m 473
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4946
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1473
29.8%
a 1000
20.2%
l 1000
20.2%
M 527
 
10.7%
F 473
 
9.6%
m 473
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4946
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1473
29.8%
a 1000
20.2%
l 1000
20.2%
M 527
 
10.7%
F 473
 
9.6%
m 473
 
9.6%

total_cholesterol
Real number (ℝ)

High correlation  Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201.08749
Minimum84.165932
Maximum354.66002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-01-31T14:13:09.538166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum84.165932
5-th percentile136.05451
Q1174.70721
median201.19155
Q3226.25171
95-th percentile265.52149
Maximum354.66002
Range270.49408
Interquartile range (IQR)51.544501

Descriptive statistics

Standard deviation40.042655
Coefficient of variation (CV)0.19913052
Kurtosis-0.015765712
Mean201.08749
Median Absolute Deviation (MAD)25.74366
Skewness0.027655354
Sum201087.49
Variance1603.4142
MonotonicityNot monotonic
2025-01-31T14:13:09.737281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
229.4636425 1
 
0.1%
253.5157626 1
 
0.1%
136.4226473 1
 
0.1%
102.8761899 1
 
0.1%
188.8562344 1
 
0.1%
188.9268087 1
 
0.1%
162.9450069 1
 
0.1%
186.3129998 1
 
0.1%
222.123983 1
 
0.1%
197.6912963 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
84.16593171 1
0.1%
87.54989835 1
0.1%
92.87641875 1
0.1%
93.12649585 1
0.1%
95.80435914 1
0.1%
98.55461411 1
0.1%
102.8761899 1
0.1%
104.0925583 1
0.1%
105.9796199 1
0.1%
106.8795273 1
0.1%
ValueCountFrequency (%)
354.6600155 1
0.1%
321.0112736 1
0.1%
318.5144453 1
0.1%
308.3995394 1
0.1%
307.7837706 1
0.1%
298.9780131 1
0.1%
294.8861215 1
0.1%
293.900023 1
0.1%
291.795429 1
0.1%
291.5010262 1
0.1%

ldl
Real number (ℝ)

High correlation  Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130.04781
Minimum36.259745
Maximum231.37663
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-01-31T14:13:09.939618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum36.259745
5-th percentile79.277339
Q1111.9632
median130.67854
Q3149.73245
95-th percentile178.28578
Maximum231.37663
Range195.11689
Interquartile range (IQR)37.769248

Descriptive statistics

Standard deviation30.041659
Coefficient of variation (CV)0.23100474
Kurtosis0.13966007
Mean130.04781
Median Absolute Deviation (MAD)18.88164
Skewness0.022657387
Sum130047.81
Variance902.5013
MonotonicityNot monotonic
2025-01-31T14:13:10.139100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
175.8791293 1
 
0.1%
202.5677245 1
 
0.1%
80.99952445 1
 
0.1%
99.58921799 1
 
0.1%
90.44801522 1
 
0.1%
104.4751928 1
 
0.1%
142.5255638 1
 
0.1%
144.3504691 1
 
0.1%
158.2465649 1
 
0.1%
134.1266731 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
36.25974518 1
0.1%
40.40926954 1
0.1%
41.16471573 1
0.1%
52.04556478 1
0.1%
52.71998014 1
0.1%
56.41520475 1
0.1%
58.29093525 1
0.1%
59.03341745 1
0.1%
59.47444059 1
0.1%
60.15072959 1
0.1%
ValueCountFrequency (%)
231.3766306 1
0.1%
229.6819142 1
0.1%
220.3809856 1
0.1%
213.5262501 1
0.1%
212.1474014 1
0.1%
209.863209 1
0.1%
206.5656072 1
0.1%
205.1694661 1
0.1%
202.5677245 1
0.1%
201.1740323 1
0.1%

hdl
Real number (ℝ)

High correlation  Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.811244
Minimum20.600644
Maximum82.31981
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-01-31T14:13:10.329904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20.600644
5-th percentile33.234771
Q142.622102
median49.682809
Q356.703598
95-th percentile66.347826
Maximum82.31981
Range61.719166
Interquartile range (IQR)14.081496

Descriptive statistics

Standard deviation10.247178
Coefficient of variation (CV)0.20572017
Kurtosis-0.024413254
Mean49.811244
Median Absolute Deviation (MAD)7.0310352
Skewness0.11671592
Sum49811.244
Variance105.00465
MonotonicityNot monotonic
2025-01-31T14:13:10.529192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.22568706 1
 
0.1%
32.992538 1
 
0.1%
65.05930135 1
 
0.1%
60.67717425 1
 
0.1%
61.64114999 1
 
0.1%
61.6450478 1
 
0.1%
48.69986082 1
 
0.1%
47.3019277 1
 
0.1%
36.16957621 1
 
0.1%
46.25507014 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
20.60064444 1
0.1%
21.28392184 1
0.1%
22.47959466 1
0.1%
22.92284932 1
0.1%
24.03641815 1
0.1%
24.75900515 1
0.1%
25.17202819 1
0.1%
26.29061168 1
0.1%
26.51030331 1
0.1%
26.74966018 1
0.1%
ValueCountFrequency (%)
82.31981044 1
0.1%
81.81782992 1
0.1%
79.75470437 1
0.1%
79.30059954 1
0.1%
79.19969099 1
0.1%
78.14483823 1
0.1%
77.54531049 1
0.1%
77.10838483 1
0.1%
76.60054035 1
0.1%
75.98135844 1
0.1%

systolic_bp
Real number (ℝ)

High correlation  Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.31269
Minimum74.43395
Maximum164.08097
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-01-31T14:13:10.716544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum74.43395
5-th percentile95.6608
Q1110.06295
median120.04217
Q3130.9118
95-th percentile147.04734
Maximum164.08097
Range89.647017
Interquartile range (IQR)20.848853

Descriptive statistics

Standard deviation15.507493
Coefficient of variation (CV)0.12889325
Kurtosis-0.16039173
Mean120.31269
Median Absolute Deviation (MAD)10.363375
Skewness0.051609367
Sum120312.69
Variance240.48234
MonotonicityNot monotonic
2025-01-31T14:13:10.915238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
124.0701272 1
 
0.1%
132.5716827 1
 
0.1%
120.2152107 1
 
0.1%
133.4459393 1
 
0.1%
121.9447384 1
 
0.1%
119.9811609 1
 
0.1%
101.9021349 1
 
0.1%
136.1178319 1
 
0.1%
149.8067744 1
 
0.1%
125.8389549 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
74.43394985 1
0.1%
74.7064485 1
0.1%
77.70363229 1
0.1%
80.18799255 1
0.1%
80.43479129 1
0.1%
82.01378748 1
0.1%
82.57254808 1
0.1%
84.00111886 1
0.1%
84.98036895 1
0.1%
85.09311375 1
0.1%
ValueCountFrequency (%)
164.0809669 1
0.1%
162.7547688 1
0.1%
162.2386929 1
0.1%
160.2155398 1
0.1%
159.6590258 1
0.1%
157.4598234 1
0.1%
157.1925754 1
0.1%
156.8924581 1
0.1%
156.8716716 1
0.1%
156.4185885 1
0.1%

diastolic_bp
Real number (ℝ)

High correlation  Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.231248
Minimum49.296305
Maximum113.84813
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-01-31T14:13:11.102787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum49.296305
5-th percentile63.736584
Q173.277119
median79.912592
Q387.084443
95-th percentile97.486383
Maximum113.84813
Range64.551822
Interquartile range (IQR)13.807323

Descriptive statistics

Standard deviation10.235917
Coefficient of variation (CV)0.12758018
Kurtosis0.053939512
Mean80.231248
Median Absolute Deviation (MAD)6.941694
Skewness0.054927942
Sum80231.248
Variance104.77399
MonotonicityNot monotonic
2025-01-31T14:13:11.286671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91.37878025 1
 
0.1%
83.99368342 1
 
0.1%
87.25891197 1
 
0.1%
79.73912377 1
 
0.1%
77.62585246 1
 
0.1%
85.17396689 1
 
0.1%
79.23553245 1
 
0.1%
76.03743117 1
 
0.1%
108.0037817 1
 
0.1%
85.55153189 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
49.29630505 1
0.1%
50.57652136 1
0.1%
51.10716365 1
0.1%
52.18529167 1
0.1%
53.55402116 1
0.1%
54.26674667 1
0.1%
54.77055517 1
0.1%
55.0806076 1
0.1%
55.23616444 1
0.1%
55.93676739 1
0.1%
ValueCountFrequency (%)
113.8481268 1
0.1%
112.3221801 1
0.1%
109.5945247 1
0.1%
108.0037817 1
0.1%
107.8610341 1
0.1%
107.3264253 1
0.1%
106.5560914 1
0.1%
106.2912783 1
0.1%
106.0016612 1
0.1%
105.4402334 1
0.1%

smoking
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Non Smoker
798 
Smoker
202 

Length

Max length10
Median length10
Mean length9.192
Min length6

Characters and Unicode

Total characters9192
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNon Smoker
2nd rowSmoker
3rd rowNon Smoker
4th rowNon Smoker
5th rowNon Smoker

Common Values

ValueCountFrequency (%)
Non Smoker 798
79.8%
Smoker 202
 
20.2%

Length

2025-01-31T14:13:11.714823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-31T14:13:11.810490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
smoker 1000
55.6%
non 798
44.4%

Most occurring characters

ValueCountFrequency (%)
o 1798
19.6%
S 1000
10.9%
m 1000
10.9%
k 1000
10.9%
e 1000
10.9%
r 1000
10.9%
N 798
8.7%
n 798
8.7%
798
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9192
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1798
19.6%
S 1000
10.9%
m 1000
10.9%
k 1000
10.9%
e 1000
10.9%
r 1000
10.9%
N 798
8.7%
n 798
8.7%
798
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9192
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1798
19.6%
S 1000
10.9%
m 1000
10.9%
k 1000
10.9%
e 1000
10.9%
r 1000
10.9%
N 798
8.7%
n 798
8.7%
798
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9192
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1798
19.6%
S 1000
10.9%
m 1000
10.9%
k 1000
10.9%
e 1000
10.9%
r 1000
10.9%
N 798
8.7%
n 798
8.7%
798
8.7%

diabetes
Boolean

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
910 
True
 
90
ValueCountFrequency (%)
False 910
91.0%
True 90
 
9.0%
2025-01-31T14:13:11.879617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

heart_attack
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
818 
True
182 
ValueCountFrequency (%)
False 818
81.8%
True 182
 
18.2%
2025-01-31T14:13:11.955569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Interactions

2025-01-31T14:13:06.337802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:00.439590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:01.852382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:02.851409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:03.870809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:04.837108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:06.623361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:00.678220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:02.008586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:03.012660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:04.023066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:04.989315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:06.845681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:00.852062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:02.169177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:03.169495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:04.178386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:05.142210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:07.064491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:01.003329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:02.321608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:03.330522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:04.339212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:05.314306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:07.351097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:01.533760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:02.521585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:03.532951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:04.505335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:05.585657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:07.558576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:01.690602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:02.698936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:03.711306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:04.680882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T14:13:05.814889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-01-31T14:13:12.043187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
agediabetesdiastolic_bphdlheart_attackldlsexsmokingsystolic_bptotal_cholesterol
age1.0000.0660.233-0.1260.1670.2420.0830.1420.3270.126
diabetes0.0661.0000.0000.0000.1330.0000.0280.0550.0860.000
diastolic_bp0.2330.0001.000-0.0890.2070.2020.0410.0710.7880.082
hdl-0.1260.000-0.0891.0000.077-0.5740.0000.026-0.073-0.465
heart_attack0.1670.1330.2070.0771.0000.1930.1020.0390.2490.192
ldl0.2420.0000.202-0.5740.1931.0000.0000.1220.2750.775
sex0.0830.0280.0410.0000.1020.0001.0000.0570.0900.029
smoking0.1420.0550.0710.0260.0390.1220.0571.0000.1300.000
systolic_bp0.3270.0860.788-0.0730.2490.2750.0900.1301.0000.184
total_cholesterol0.1260.0000.082-0.4650.1920.7750.0290.0000.1841.000

Missing values

2025-01-31T14:13:07.943875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-31T14:13:08.243191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

agesextotal_cholesterolldlhdlsystolic_bpdiastolic_bpsmokingdiabetesheart_attack
057Male229.463642175.87912939.225687124.07012791.378780Non SmokerNoNo
158Male186.464120128.98491634.95096895.49255264.355040SmokerNoNo
237Male251.300719152.34759245.91328899.51933564.953147Non SmokerYesNo
355Male192.058908116.80368467.208925122.46000273.821382Non SmokerNoNo
453Male151.203449107.01739660.693838123.02225781.121946Non SmokerYesYes
539Male236.033455153.88080931.208614121.85739679.589069Non SmokerNoNo
665Female174.615666114.02940855.692586135.60505085.529955Non SmokerNoNo
733Female242.919402147.95137554.439475123.51155777.331714Non SmokerNoNo
849Female95.80435983.30487560.758929111.69748877.630529SmokerNoNo
955Female181.360943106.01178250.576747129.57641887.588781Non SmokerNoNo
agesextotal_cholesterolldlhdlsystolic_bpdiastolic_bpsmokingdiabetesheart_attack
99037Male184.883473122.70406550.522589122.42461792.429249Non SmokerYesNo
99162Female220.644026125.79439049.313152105.30625175.079786SmokerNoNo
99235Female177.508745115.64000653.369375101.51309974.243940Non SmokerYesNo
99383Female211.539050170.99992656.547721134.10549887.977523Non SmokerNoYes
99447Female211.297642145.36017848.992366104.84620265.955891Non SmokerNoNo
99565Male195.336429149.07095143.914928132.87844086.246414Non SmokerNoNo
99660Male192.342928134.35739453.380714145.14953591.069141Non SmokerNoNo
99770Female174.179319125.90004750.406918133.02287885.851330Non SmokerNoNo
99848Female189.715685152.38873740.700912113.52796586.368294Non SmokerNoNo
99963Male136.20772496.76863557.566116136.43950798.995934Non SmokerNoNo